A two-step super-Gaussian independent component analysis approach for fMRI data

NeuroImage

Volumn 118, Issue , 2015, Pages 344-358

  Ge, Ruiyang ^a   Yao, Li ^a   Zhang, Hang ^b   Long, Zhiying ^a  

^a BEIJING NORMAL UNIVERSITY   (China)

^b SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

Author keywords

ATGP; FMRI; ICA; Sparsity; Super Gaussian

Indexed keywords

ADULT; ALGORITHM; ARTICLE; BRAIN MAPPING; CONTROLLED STUDY; FAST INDEPENDENT COMPONENT ANALYSIS; FEASIBILITY STUDY; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HAND MOVEMENT; HUMAN; HUMAN EXPERIMENT; INDEPENDENT COMPONENT ANALYSIS; INFOMAX INDEPENDENT COMPONENT ANALYSIS; INTERMETHOD COMPARISON; KERNEL METHOD; MALE; MATHEMATICAL MODEL; MEAN FIELD INDEPENDENT COMPONENT ANALYSIS; NORMAL HUMAN; NUCLEAR MAGNETIC RESONANCE SCANNER; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SPARSE ONLINE DICTIONARY LEARNING; SUPER GAUSSIAN INDEPENDENT COMPONENT ANALYSIS; TWO STEP SUPER GAUSSIAN INDEPENDENT COMPONENT ANALYSIS; YOUNG ADULT; BRAIN; COMPUTER SIMULATION; MOTOR ACTIVITY; NONLINEAR SYSTEM; NORMAL DISTRIBUTION; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PROCEDURES; SIGNAL PROCESSING; STATISTICS;

ALGORITHMS; BRAIN; BRAIN MAPPING; COMPUTER SIMULATION; HUMANS; MAGNETIC RESONANCE IMAGING; MOTOR ACTIVITY; NONLINEAR DYNAMICS; NORMAL DISTRIBUTION; SIGNAL PROCESSING, COMPUTER-ASSISTED; STATISTICS AS TOPIC;

EID: 84934957131     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2015.05.088     Document Type: Article

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